Corsi, Fenwick and Gramsci: How bloggers and advanced analytics are changing the National Hockey League
Bibliographic record
Abstract
Since the early 2000s, an ever growing online community of bloggers and amateur statisticians has been developing a new set of advanced analytics and performance metrics for the National Hockey League. Many of the people who are driving innovation in this field are not data scientists but, rather, intellectually curious fans of the game, who are playing a significant role in reshaping the way the game is consumed and understood. Yet, despite the body of knowledge created online, only within the last few years have the National Hockey League and the mainstream sport media begun to take notice of these innovations. I argue that the analytics movement is being driven from the fans up, rather than from the National Hockey League and other professional leagues down, and that the drivers of this movement are examples of what Antonio Gramsci calls ‘organic intellectuals’ – the analytics camp is locked into its own ‘war of position’ against the hegemony of traditional hockey fans, coaches, management and sport media. My research explores the resistance Internet-based content creators have experienced from established hockey media personalities (‘Hockey Men’) and the National Hockey League itself, connecting this resistance to a growing trend away from evidence-based discourse in the current Western media landscape.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".